Blackwell's original regret minimization procedure [1]} and its various extensions (e.g. [2]} ([2]}), [4]} ([4]}), [6]} ([6]})) and applications to online learning [8]} typically assign equal weight to each iteration of the procedure. Recently, [9]} ([9]}) demonstrated that modifying the weight schedule of regret matching empirically resulted in faster convergence to equilibria while maintaining a similar worst-case convergence bound. However, this modified schedule was fixed and pre-determined before the start of the procedure. Our algorithm extends this direction of inquiry by greedily choosing the iteration weights to minimize a function of regret at runtime.
